KR20220165908A - System for selection of vertiports of urban air mobility - Google Patents

Abstract

According to an embodiment of the present invention, a system for selection of vertiports of urban air mobility includes: a data processing unit which collects and analyzes population data in an area where urban air mobility is expected to be used; a data clustering unit dividing the population data into the same number of clusters as the number of vertiports of the urban air mobility; a clustering result evaluation unit evaluating a clustering result of the population data obtained by the data clustering unit; and a clustering result adjustment unit for determining whether to adjust the position of the vertiports according to the clustering result of the population data obtained by the data clustering unit. Thus, traffic congestion can be relieved while minimizing infrastructure construction costs.

Description

Urban air mobility vertical takeoff and landing site selection system {SYSTEM FOR SELECTION OF VERTIPORTS OF URBAN AIR MOBILITY}

The present invention relates to a system for selecting a vertical take-off site for urban air mobility, and more particularly, analyzes demand data based on mobile demographics such as commuting or school commuting, clusters the demand data, and sets the center of the data belonging to the cluster to the location of the vertical landing pad. Provides a system for selecting the location of the vertical take-off and landing site for urban air mobility.

Large cities around the world, including Seoul, have reached a state of population saturation due to population density in large cities, and as a result, economic losses continue to increase due to increased commuting time and increased traffic congestion costs. Therefore, in order to solve this problem, research on transportation means using the air instead of the ground is being actively conducted, and this is Urban Air Mobility (UAM).

As urban air mobility (UAM), electric vertical take-off and landing (eVTOL) or future personal air vehicle (PAV) can be operated.

However, since urban air mobility creates a new means of transportation that did not exist before, an infrastructure that can support it is needed. That is, in order to operate an eVTOL or PAV, vertical take-off and landing pads (Vertiports), which are service infrastructure facilities that can not only take off and land, but also charge and board and wait for passengers, are required.

However, there is a lack of technology or research on vertical take-off and landing pads, which are necessary infrastructure for commercialization of urban air mobility. In particular, there is a problem that it is extremely rare that a technology for selecting a location where a vertical take-off and landing pad is to be installed is proposed.

The present applicant has proposed the present invention in order to solve the above problems.

Japanese Patent Publication No. 2021-503677 (2021.02.12.)

The present invention has been devised to solve the above problems, and by selecting an optimal location that can satisfy the commuting demand for the location of the vertical take-off pad, which is an essential infrastructure for the operation of urban air mobility, while minimizing the construction cost of the vertical take-off pad infrastructure. It provides an urban air mobility vertical take-off site selection system that can relieve congestion and reduce commuting time for commuters and commuters.

The present invention provides an urban air mobility vertical takeoff location selection system capable of selecting the location of a vertical takeoff pad optimized for the commuting population so as to minimize the construction cost of the vertical takeoff pad and produce the maximum effect.

The present invention provides an urban air mobility vertical take-off landing site selection system that can be applied to all large cities around the world.

City air mobility vertical takeoff and landing site location selection system according to an embodiment of the present invention for achieving the above object is a data processing unit for collecting and analyzing population data of an area where urban air mobility is expected to be used; a data clustering unit dividing the population data into the same number of clusters as the number of vertical takeoff and landing sites of the urban air mobility; a clustering result evaluation unit evaluating a clustering result of the population data obtained by the data clustering unit; and a clustering result adjusting unit that determines whether to adjust the position of the vertical take-off site according to the clustering result of the population data obtained by the data clustering unit.

The data processing unit may include: a population data collection unit that collects commuting and school population data of the urban air mobility usage expected area; a data filtering unit for excluding people from the commuting and schooling population who have the same residence, commuting and schooling places, people close to their residence and commuting and going to school, or people under a specific age; a data number control unit for adjusting the number of digits of the population number by dividing the number of people filtered by the data filtering unit by the reference population number; and a region and data matching unit that matches the number of population data obtained by the number of data adjusting unit with the adjusted number of digits to a residence.

The data number adjusting unit obtains a value obtained by rounding off a quotient obtained by dividing the filtered population number by the reference population number, and the reference population number may be changed according to a clustering result of the data clustering unit.

The area and data matching unit displays the administrative district of the residence as a square having the same area as the actual area in the administrative district of the residence, and the population data corresponding to the rounded value is based on the center of the square administrative district of the residence. can be evenly distributed in a square administrative district.

The data clustering unit may select the location of the vertical take-off site through clustering and display the location in the expected urban air mobility use area.

The data clustering unit randomly designates the same number of centers as the number of vertical take-off pads among the population data corresponding to the rounded value, and performs clustering to assign the population data to a cluster to which the nearest center among the centers belongs, According to the result of clustering, the center of the population data is newly designated, and clustering is performed to reassign the population data to the cluster to which the closest center among the newly designated centers belongs, and the clustering is repeated until the center does not change any more. can do.

The clustering result evaluation unit may evaluate a clustering result of the population data corresponding to the rounded value by using a silhouette technique.

The clustering result adjustment unit compares the location of the vertical takeoff and landing site according to the clustering result of the population data corresponding to the rounded value obtained by the data clustering unit with a satellite image or an aerial photograph of the expected urban air mobility use area, and the vertical It is possible to determine whether to adjust the location by reviewing the geographical conditions of the airfield.

The clustering result adjustment unit analyzes satellite photos or aerial photos of the area where the urban air mobility is expected to be used, obtains geographic information including facilities, development restricted areas, no-fly areas, military restricted areas, or residential areas, and obtains the latitude of the geographic information. / It is possible to determine whether to adjust the position of the vertical take-off and landing site by comparing the longitude value and the latitude/longitude value of the vertical take-off and landing site.

The clustering result adjusting unit may adjust the priority or location of the vertical take-off and landing site according to the operational purpose of the urban air mobility.

Since the urban air mobility vertical take-off site selection system according to the present invention selects an optimal location that can meet the essential mobility demand such as commuting to work or school, it minimizes the cost of vertical lift infrastructure construction while relieving traffic congestion and reducing traffic congestion. It can reduce commuting time.

Since the urban air mobility vertical takeoff site selection system according to the present invention selects a location optimized for the commuting or school population as the vertical takeoff site location, the vertical takeoff and landing site can produce the maximum effect at the minimum cost.

The urban air mobility vertical takeoff and landing site selection system according to the present invention can be applied not only to Seoul and the metropolitan area, but also to all large cities around the world if data related to commuting or schooling population is secured.

1 is a diagram schematically showing the configuration of a system for selecting a location for a vertical takeoff and landing site for urban air mobility according to an embodiment of the present invention.
FIG. 2 is a diagram showing a state in which commuting and schooling populations are expressed in an analysis target region in which an urban air mobility vertical take-off and landing site is expected to be needed in the system according to FIG. 1 .
FIG. 3 is a diagram showing a state in which an analysis target area, which is expected to require an urban air mobility vertical take-off and landing site in the system according to FIG. 1, is expressed as a square corresponding to the width of an administrative district.
4 and 5 are diagrams showing clustering results according to the number of vertical takeoff pads in the system according to FIG. 1 .
6 and 7 are diagrams showing silhouette evaluation results for clustering results according to the number of vertical takeoff pads in the system according to FIG. 1 .
FIG. 8 exemplarily shows geographic information of an analysis target region in which an urban air mobility vertical take-off and landing site is expected to be needed in the system according to FIG. 1 .
9 and 10 are views showing results of adjusting the position of the vertical take-off and landing pad in the system according to FIG. 1 .
FIG. 11 is a flowchart illustrating a method for selecting a location of an urban air mobility vertical takeoff and landing site using the system according to FIG. 1 .

Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings, but the same or similar reference numerals are given to the same or similar components, and overlapping descriptions thereof will be omitted. The suffix "part" for components used in the following description is given or used interchangeably in consideration of ease of writing the specification, and does not itself have a meaning or role distinct from each other. In addition, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the embodiment disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, the technical idea disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and technical scope of the present invention , it should be understood to include equivalents or substitutes.

Terms including ordinal numbers, such as first and second, may be used to describe various components, but the components are not limited by the terms. These terms are only used for the purpose of distinguishing one component from another.

It should be understood that when an element is referred to as being “connected” to another element, it may be directly connected to the other element, but other elements may exist in the middle.

Singular expressions include plural expressions unless the context clearly dictates otherwise.

In this application, terms such as "comprise" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

It is advised that the drawings are schematic and not drawn to scale. Relative dimensions and proportions of parts in the drawings are shown exaggerated or reduced in size for clarity and convenience in the drawings, and any dimensions are illustrative only and not limiting. And like structures, elements or parts appearing in two or more drawings, like reference numerals are used to indicate like features.

The embodiments of the present invention specifically represent ideal embodiments of the present invention. As a result, various modifications of the drawings are expected. Therefore, the embodiment is not limited to the specific shape of the illustrated area, and includes, for example, modification of the shape by manufacturing.

1 is a diagram schematically showing the configuration of a system for selecting a location for a vertical take-off and landing site for urban air mobility according to an embodiment of the present invention, and FIG. 2 is a diagram showing the configuration of an urban air mobility vertical take-off and landing site in the system according to FIG. Figure 3 is a diagram showing a state in which a city air mobility vertical takeoff and landing site is expected to be needed in the system according to FIG. 1, expressed as a square corresponding to the width of an administrative district. 4 and 5 are views showing clustering results according to the number of landing pads in the system according to FIG. 1, and FIGS. 6 and 7 are views showing silhouette evaluation results for the clustering results according to the number of landing pads in the system according to FIG. 1. Figure 8 is a diagram showing geographic information of an analysis target region where an urban air mobility vertical take-off pad is expected to be needed in the system according to FIG. 1 by way of example, and FIGS. FIG. 11 is a diagram showing an adjustment result, and is a flowchart illustrating a method of selecting a location for a vertical take-off and landing site for urban air mobility using the system according to FIG. 1 .

Referring to FIG. 1 , the urban air mobility vertical takeoff and landing site location selection system 100 according to an embodiment of the present invention includes a data processing unit 110 that collects and analyzes population data expected to use urban air mobility (UAM) ; a data clustering unit 170 that divides the population data into the same number of clusters as the number of the urban air mobility vertical take-off and landing sites; a clustering result evaluation unit 180 that evaluates a clustering result of the population data obtained by the data clustering unit 170; and a clustering result adjusting unit 190 that determines whether to adjust the location of the vertical take-off site according to the clustering result of the population data obtained by the data clustering unit.

Urban air mobility vertical take-off landing site selection system (100, hereinafter abbreviated as "location selection system") according to an embodiment of the present invention selects the location of a vertical take-off site, which is an essential infrastructure for the operation of urban air mobility. is to do The positioning system 100 according to an embodiment of the present invention largely includes data collection and analysis, clustering using K-average algorithm, silhouette technique for evaluating clustering, and landing pad location adjustment, or a part that performs them, can be configured.

In the following, the demand for eVTOL or PAV is analyzed by surveying the number of commuters and school commuters provided by the National Statistical Office, centering on the metropolitan area where 50% of the population of Korea lives, and using the data and the K-average algorithm, necessary for urban air mobility operation The location selection system 100 for selecting the location of the vertical takeoff and landing pad will be described.

As shown in FIG. 1, the data processing unit 110 of the location selection system 100 according to an embodiment of the present invention, commuting and commuting population data of the urban air mobility expected use area (ie, analysis target area) Population data collection unit 120 for collecting; a data filtering unit 130 for excluding people who live in the same or close to the same or close to their place of residence or who are younger than a specific age from among the number of commuting and schooling populations; a data number adjusting unit 140 that reduces the number of digits of the population number by dividing the number of people filtered by the data filtering unit 130 by the reference population number; and a region and data matching unit 150 that matches the population data obtained from the data number adjusting unit 140 to a residence.

Here, the data processing unit 120 includes the filtered population data obtained from the data filtering unit 130, the population data obtained from the data number control unit 140, and the map and data of the analysis target area obtained from the region and data matching unit 150. A data display unit 160 displaying information on the location of the vertical take-off and landing field obtained by the clustering unit 170 may be further included. The data display unit 160 may be provided in the form of a display.

On the other hand, if the introduction of urban air mobility becomes common, the use that is expected to have the highest demand is commuting and commuting. Therefore, in order to find out the demand for eVTOL or PAV for commuting and commuting to school, the data processing unit 110 of the location selection system 100 according to an embodiment of the present invention determines the number of commuting and commuting population by current residence/commuting location. data can be collected. To this end, the population data collection unit 120 may be provided to access the population data base of the National Statistical Office.

Hereinafter, the location selection system 100 according to an embodiment of the present invention using the data of the commuting population survey by current residence/commuting school conducted by the National Statistical Office in 2015 will be described as an example. do.

If the population data collected by the population data collection unit 120 from the National Statistical Office database is the commuter population data by residence/commuting school surveyed in 2015, the number of commuters and commuters in 79 cities, counties, and districts in the Seoul metropolitan area is 14,335,975. Population data can be collected.

However, if the commuting and schooling locations collected by the population data collection unit 120 are in the same administrative district as their residence or if the commuting and schooling locations are close to the residence, in most cases commuting and schooling do not take much time, so commuters are You may not use eVTOL or PAV. In addition, when commuters are young (eg, children under the age of 12), eVTOLs or PAVs may not be used because they do not move alone or do not move far.

The data filtering unit 130 may obtain filtered population data except for the number of people unlikely to use urban air mobility in the analysis target area. To this end, the data filtering unit 130 may receive information about the address and age of the commuter or school commuter in the area to be analyzed and use it for filtering. At this time, information that needs to be kept confidential according to the Personal Information Protection Act is not input to the data filtering unit 130 .

When filtering is applied to the population data of 14,335,975 people, the output population data is reduced to 7,959,469 people in the data filtering 130 . Therefore, the region and data matching unit 150 or the data display unit 160 displays or visualizes the data of 7,959,469 people on a map or display of the area to be analyzed, but cannot be displayed individually because there is too much data. . In order to solve this visual problem, the data processing unit 110 of the location selection system 100 according to an embodiment of the present invention divides the number of population filtered by the data filtering unit 130 by the reference population number to reduce the number of digits of the population number. A number control unit 140 may be provided.

For example, the data number control unit 140 rounds the population data allocated to each administrative district belonging to the analysis target area and expresses it on the map as one dot per 5,000 people (standard population), in which case the total number of dots is 1,388.

In this way, the data number control unit 140 obtains a value obtained by rounding up the quotient obtained by dividing the population number (7,959,469 people) filtered by the data filtering unit 130 by the reference population number (5,000 people). This becomes the 1,388 points described above.

2 shows the population commuting to and from school in the metropolitan area of Korea as one dot per 5,000 people on a map. A total of 1,388 points are marked on the map in the metropolitan area. The data display unit 160 may display 1,388 points on a map using MATLAB.

Here, the data number control unit 140 may change the size or value of the reference population number according to the clustering result of the data clustering unit 170 to be described later. If the size of the reference population is too large, the rounded value obtained by dividing by the reference population is too small, resulting in insufficient data to perform clustering. . Therefore, the data number adjusting unit 140 may change the size of the reference population number according to the clustering result of the data clustering unit 170 or the evaluation result of the clustering result evaluation unit 180 to be described later. For example, when clustering is not properly performed as a result of evaluation by the clustering result evaluation unit 180, the data number adjusting unit 140 may increase the number of clustering data by reducing the reference population number.

On the other hand, the area and data matching unit 150 displays the administrative district of the residence as a square having the same area as the actual area in the administrative district of the residence, and displays population data corresponding to the rounded value of the residence. Based on the center of the square administrative district, it can be evenly distributed within the square administrative district.

Since the shapes of the administrative districts in the area to be analyzed, that is, the metropolitan area, are not identical and irregular, the population data corresponding to the rounded value (i.e., clustering data: meaning data to be clustered) is uniformly distributed within the administrative district. It can be difficult to distribute or to scatter in the center of an administrative district. To solve this problem, the region and data matching unit 150 may express each administrative district as a square having the same size as the area of the administrative district in order to simply express each administrative district having an irregular shape.

3 is a representation of administrative districts in the metropolitan area as squares having the same area as the area. The region and data matching unit 150 may express an administrative district as a square having the same area using MATLAB. At this time, the region and data matching unit 150 distributes and matches the clustering data belonging to each administrative district represented by a square. It is preferable to distribute the clustering data evenly or close to the center of the square. Do.

[Table 1] shows the number of commuters and commuters and the number of data in major administrative districts in the metropolitan area, which are the areas to be analyzed.

administrative district commuting and commuting population number of clustering data Gangnam-gu 609,119 122 Jung-gu 352,261 70 Seocho-gu 300,469 60 Jongno-gu 282,783 57 Seongnam City 231,130 46 Namdong-gu 112,460 22

The data clustering unit 170 of the location selection system 100 according to an embodiment of the present invention selects the location of the vertical take-off site through clustering of population data (clustering data) corresponding to the rounded value and provides urban air mobility It can be marked in the area to be analyzed, which is the area where it is expected to use.

The population data collected as above is the number of commuters and commuters in each administrative district, but it is closely related to the demand for eVTOL or PAV when urban air mobility is commercialized. Therefore, vertical take-off and landing sites must be located in appropriate locations to meet the demands of eVTOLs or PAVs. To this end, the data clustering unit 170 may cluster the clustering data and express them on a map. At this time, the technique used in the data clustering unit 170 is a K means algorithm.

The data clustering unit 170 minimizes the mean square Euclidean distance between the data given through the clustering process for the population data (clustering data) corresponding to the rounded value and the centroids of the clusters to which each data is assigned. will do the process.

로 정의된다.Here, the cluster centroid is the mean or centroid of the data in the cluster ω as shown in [Equation 1].

is defined as

The measure of how well the centroid of each cluster represents the data belonging to each cluster is the residual sum of squares (RSS), which is the squared distance of the difference between all vectors and the cluster centroid of each vector, as shown in [Equation 2]. of squares), and reducing this value is the purpose of the K-means algorithm.

The data clustering unit 170 may select the location of the vertical takeoff pad by applying a K-average algorithm to the population data (clustering data) corresponding to the rounded value. At this time, a parameter called k must be initially designated, where k is the number of clusters and the number of vertical takeoff pads at the same time. Accordingly, the data clustering unit 170 may first determine the number of vertical takeoff pads in order to perform clustering, and then perform clustering using the numerical value.

The data clustering unit 170 may designate an arbitrary k value by using a rule of thumb for determining an approximate k value for clustering or the elbow method for sequentially increasing the k value, which is the number of clusters, and checking the result of clustering. there is.

The data clustering unit 170 randomly designates the center of the number (k) equal to the number (k) of the vertical take-off pads among the population data (clustering data) corresponding to the rounded value, and sets the population data (clustering data) as Clustering can be performed in which the closest centroid is assigned to the cluster to which it belongs. That is, the data clustering unit 170 divides the clustering data into as many clusters as the number of landing pads, but performs clustering so that the data of each cluster is located close to the center of the cluster. In addition, the data clustering unit 170 newly designates the center of the population data (clustering data) belonging to each cluster according to the result of clustering, and returns the population data belonging to each cluster to the cluster to which the center closest to the newly designated center belongs. Clustering is performed by assigning data. The data clustering unit 170 may repeatedly perform clustering until the centers of clusters do not change any more.

In other words, the data clustering unit 170 randomly designates k centroids in the clustering data (process 1). Next, the clustering data is assigned to the cluster (group) to which the nearest centroid belongs (process 2). Based on the result of the clustering data assigned in step 2, the center of the cluster is newly designated (step 3). The process is repeated 2 to 3 times until the center of the cluster does not change any more.

For example, if k is set to 3, the data clustering unit 170 will create three cluster centers by randomly designating clustering data located at random places as centers. Widely spread clustering data is clustered based on the centroid. After that, the data clustering unit 170 calculates the average of the clustered data groups and moves the center of the cluster to the average value. If the center has changed, clustering is performed by gathering clustering data around the center again based on the center that has moved toward the average, and the new center is moved to the average value of the cluster. This process is repeated infinitely until the center does not change to create three groups of clustering data. Here, the center of the clustering data belonging to the final group (cluster) becomes the location of the vertical takeoff pad.

On the other hand, if urban air mobility is fully commercialized in Sao Paulo, Brazil, which has a population of 21 million, similar to Korea's metropolitan area, about 1,050 PAVs will be needed to replace various means of transportation such as private cars, public transportation, and commuting. Research results have been known to utilize helipads in existing transportation hubs. In addition, according to this study, 5 vertical take-off and landing pads for 120 eVTOLs were initially planned, and then the number of vertical take-off and landing pads could be increased to 40 and eventually to 100. Accordingly, the data clustering unit 170 may determine that 100 vertical takeoff and landing sites similar to those of Sao Paulo in the metropolitan area of Korea having a similar population may be appropriate based on the research results. In this case, k becomes 100.

In addition, the data clustering unit 170 may determine an optimal k value by arbitrarily setting a k value and evaluating a clustering result performed accordingly. For example, clustering may be performed when k values are 5, 40, and 100, and the optimal k may be determined by comparing the results. 4 and 5 show how the clusters are formed by dividing the clustering data by applying the K-average algorithm in the data clustering unit 170 into cases where k values are 5, 40, and 100. Figure 4 (a) is the result of clustering when the number of vertical takeoff pads is 5, Figure 4 (b) is the result of clustering when the number of vertical takeoff pads is 40, Figure 5 is the result of clustering when the number of vertical takeoff pads is 100 show the result.

Referring to FIGS. 4 and 5 , clustering data within each cluster is designated as a dot of the same color, and a vertical take-off field designated as k is marked with a black star. There are various ways to build vertical take-off infrastructure in the metropolitan area, but in most cases, huge costs will be required because it is an urban area. Therefore, it is very important to specify the parameter k, the exact number of vertical takeoff pads.

Meanwhile, the data clustering unit 170 can finally determine the location of the vertical take-off pad through an iterative process only when the k parameter is determined. Therefore, it is necessary to evaluate how well the clustering is performed with a given k value. To this end, the positioning system 100 according to an embodiment of the present invention may include a clustering result evaluation unit 180 .

The clustering result evaluation unit 180 may evaluate a clustering result of population data (clustering data) corresponding to the rounded value by using a silhouette method.

The clustering result evaluation unit 180 may evaluate the clustering result using [Equation 3] and [Equation 4].

In [Equation 3] and [Equation 4], i is each clustering data, a(i) is the average distance from other data in the cluster to which data (i) is assigned, and b(i) is data (i) and Among the averages of distances to data in a cluster other than the cluster to which data (i) is assigned, s(i), which is the closest, is the evaluation result applying the silhouette technique.

The value s(i) of [Equation 3] is defined as a value between -1 and 1. Here, the closer s(i) is to 1, the better the clustering, and the closer to -1, the poorer the clustering. The clustering result evaluation unit 180 may perform this process using the MATLAB silhouette function.

The silhouette technique was performed on the three clustering results mentioned above, and the results are shown in FIGS. 6 and 7 . Figure 6 (a) is the silhouette evaluation result when the number of vertical take-off and landing pads is 5, Figure 6 (b) is the silhouette evaluation result when the number of vertical take-off and landing pads is 40, Figure 7 is the number of vertical take-off pads 100 It shows the silhouette evaluation result at the time.

Referring to FIGS. 6 and 7 , each silhouette value of 1,388 pieces of data is grouped into a certain range and shown as a bar graph. Through the histograms shown in FIGS. 6 and 7, it can be seen that the silhouette value is closer to 1 than -1 as a result of clustering with 5, 40, and 100 objects.

The clustering result evaluation unit 180 may represent the silhouette value as an average in order to further specify the silhouette value. When the silhouette values shown in FIGS. 6 and 7 are expressed as averages, they are 0.4500, 0.5670, and 0.5291, respectively, which shows that the clustering is good.

The data display unit 160 may display the location of the vertical take-off site analyzed through clustering on a map of the area to be analyzed, and the location may be displayed using latitude and longitude values through MATLAB.

Here, since the data clustering unit 170 selects the location of the vertical take-off and landing field simply by using the K-average algorithm, it is necessary to select the location of the vertical take-off and landing field by reflecting actual geographical conditions. To this end, the positioning system 100 according to an embodiment of the present invention may include a clustering result adjusting unit 190 .

The clustering result adjustment unit 190 determines the location of the vertical take-off site according to the clustering result of the population data (clustering data) corresponding to the rounded value obtained by the data clustering unit 170 and the expected area of urban air mobility use (area to be analyzed). It is possible to determine whether or not to adjust the location by comparing satellite images of the airfield and reviewing the geographical conditions of the vertical take-off and landing site.

For example, in the case of Korea, since there is a green belt designated as a development restriction area in the metropolitan area as shown in FIG. In addition, in the case of Seoul, where most of the population of Korea resides, if a vertical take-off site is built in a densely populated residential area, there is a high possibility of problems caused by noise such as PAVs, so it is necessary to exclude downtown areas. Therefore, areas near highways, rivers, and streams can be potential candidates for vertical take-off and landing sites.

The clustering result adjustment unit 190 analyzes satellite photos or aerial photos of the expected urban air mobility use area (analysis target area) to obtain geographic information including facilities, development restricted areas, no-fly areas, military restricted areas, or residential areas. and comparing the latitude/longitude values of the geographic information and the latitude/longitude values of the vertical take-off and landing site to determine whether to adjust the location of the vertical take-off and landing site.

In the positioning system 100 according to an embodiment of the present invention, the data clustering unit 170 determines the actual location of the vertical take-off site clustered by using the K-average algorithm, and then the clustering result adjusting unit 190 determines the actual location. If it is determined that it is impossible to actually construct a vertical take-off pad, the clustering process can be retried. The data clustering unit 170 performs clustering using the K-means algorithm and must provide an initial position to start the iterative task. Therefore, it is possible to select a vertical take-off pad at a changed location just by retrying the clustering process. At this time, the clustering result adjusting unit 190 finds an appropriate site around the center of the cluster presented by MATLAB as a starting point and selects it as the final location of the vertical take-off pad.

9(a) is the location of the final vertical take-off landing site where the position adjustment process of each vertical landing pad was performed with respect to the result of clustering with the previously performed five vertical landing pads, and FIG. 9 (b) is the location of the previously performed 40 vertical landing pads 10 shows the final vertical take-off and landing site position adjustment process for each vertical take-off and landing site for the results of clustering with 100 vertical take-off and landing pads. Shows the location of the take-off and landing pads.

The location selection system 100 according to an embodiment of the present invention selects the final location as shown in FIG. 10 when the number of vertical takeoff and landing pads is 100. The location of FIG. 10 is a location selected by avoiding all cases of geographical obstacles. . The location of such a vertical takeoff and landing site may include the vicinity of Hangang Park, a cloverleaf of a highway IC, a parking lot on the roof of a building, or a heliport in a high-rise building.

As such, the positioning system 100 according to an embodiment of the present invention periodically updates information on locations where a vertical take-off pad cannot be built due to various constraints and stores the updated information in the clustering result adjusting unit 190. And by using it when selecting the location of the vertical take-off and landing site, the optimal location of the vertical take-off and landing site can be selected.

When the clustering result adjustment unit 190 determines that the location of the vertical take-off pad needs to be adjusted, the location to be adjusted may be selected as a location adjacent to the previous location or a new location unrelated to the previous location. In this case, the location selection system 100 according to an embodiment of the present invention may repeatedly perform clustering only for administrative districts or clusters requiring location adjustment.

In addition, the clustering result adjustment unit 190 may adjust the priority or location of the vertical take-off and landing site according to the operational purpose of urban air mobility. That is, the clustering result adjustment unit 190 not only adjusts the location of the vertical takeoff and landing site in consideration of whether or not there is a geographical obstacle, but also adjusts the priority or location of the vertical takeoff and landing site according to the operational purpose of urban air mobility regardless of the geographical obstacle. . For example, when urban air mobility is operated as an airport shuttle for airport users, the required vertical take-off and landing site can be located adjacent to the airport. In addition, when urban air mobility is operated as an air ambulance for patient transfer, the vertical take-off and landing site required can be selected in a location adjacent to the hospital.

In this way, the clustering result adjustment unit 190 selects a location in consideration of geographical installation constraints when urban air mobility is operated for commuting and commuting, while normal commuting and commuting, such as an airport shuttle or air ambulance, are performed. If it is used for other purposes, the location can be selected with priority given to the original purpose rather than geographical installation constraints. To this end, information on the operational purpose of urban air mobility as well as information on geographical installation constraints may be input to the clustering result adjustment unit 190, and the location of the vertical takeoff pad is selected by comparing and prioritizing the information. can do.

The positioning system 100 according to an embodiment of the present invention can use MATLAB to visualize all the processes described above, and the K-average algorithm and silhouette technique can use functions built into MATLAB. In addition, the location of the selected vertical take-off and landing pad can be identified using latitude and longitude values.

The system described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, systems and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. You can command the device. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

Meanwhile, FIG. 11 shows a flow chart of a method for selecting the location of an urban air mobility vertical takeoff and landing site using the location selection system 100 according to an embodiment of the present invention.

Referring to FIG. 11, a method for selecting a location for an urban air mobility airfield according to an embodiment of the present invention includes collecting population data (1100), filtering population data (1200), and adjusting population data. (1300), matching region and population data (1400), displaying population data (1500), clustering population data (1600), evaluating clustering results (1700), and adjusting clustering results It may include step 1800 of doing.

Each of the above steps includes the population data collection unit 120, data filtering unit 130, data number control unit 140, region and data matching unit 150 of the location selection system 100 according to an embodiment of the present invention. ), the data display unit 160, the data clustering unit 170, the clustering result evaluation unit 180, and the clustering result adjustment unit 190.

A description of each step of the method for selecting a location for an urban air mobility vertical takeoff and landing site according to an embodiment of the present invention is similar to or the same as that of the location selection system 100 according to an embodiment of the present invention, so repetitive descriptions are omitted. do.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CDROMs and DVDs, and magnetic-optical media such as floptical disks. Included are hardware devices specially configured to store and execute program instructions, such as magneto-optical media and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, in one embodiment of the present invention, specific details such as specific components and limited embodiments and drawings have been described, but this is only provided to help a more general understanding of the present invention, and the present invention is based on the above embodiments. It is not limited, and those skilled in the art can make various modifications and variations from these descriptions. Therefore, the spirit of the present invention should not be limited to the described embodiments and should not be determined, and all things equivalent or equivalent to the claims as well as the following claims belong to the scope of the present invention.

100: Urban air mobility vertical take-off and landing site selection system
110: data processing unit 120: population data collection unit
130: data filtering unit 140: data number control unit
150: region and data matching unit 160: data display unit
170: data clustering unit 180: clustering result evaluation unit
190: clustering result adjustment unit

Claims (10)

a data processing unit that collects and analyzes population data in an area where urban air mobility is expected to be used;
a data clustering unit dividing the population data into the same number of clusters as the number of vertical takeoff and landing sites of the urban air mobility;
a clustering result evaluation unit evaluating a clustering result of the population data obtained by the data clustering unit; and
a clustering result adjustment unit determining whether to adjust the position of the vertical take-off site according to the clustering result of the population data obtained by the data clustering unit;
Urban air mobility vertical take-off landing site selection system comprising a.
According to claim 1,
The data processing unit,
a population data collection unit that collects data on the number of commuting and commuting populations in the expected urban air mobility usage area;
a data filtering unit for excluding people from the commuting and schooling population who have the same residence, commuting and schooling places, people close to their residence and commuting and going to school, or people under a specific age;
a data number control unit for adjusting the number of digits of the population number by dividing the number of people filtered by the data filtering unit by the reference population number; and
a region and data matching unit that matches the number of population data obtained by the number of data adjusting unit with the adjusted number of digits to a residence;
Urban air mobility vertical take-off landing site selection system comprising a.
According to claim 2,
The data number controller obtains a value obtained by dividing the filtered population by the reference population and rounding off the quotient,
Wherein the reference population number is changeable according to the clustering result of the data clustering unit.
According to claim 3,
The area and data matching unit,
Indicate the administrative district of the residence as a square having the same area as the actual area in the administrative district of the residence;
The urban air mobility vertical take-off site location selection system, characterized in that for evenly distributing the population data corresponding to the rounded value within a square administrative district based on the center of the square administrative district of the residence.
According to claim 4,
Wherein the data clustering unit selects the location of the vertical take-off and landing site through clustering and displays it in the urban air mobility expected use area.
According to claim 5,
The data clustering unit randomly designates the same number of centers as the number of vertical take-off pads among the population data corresponding to the rounded value, and performs clustering to assign the population data to a cluster to which the nearest center among the centers belongs,
According to the result of clustering, the center of the population data is newly designated, and clustering is performed to reassign the population data to the cluster to which the closest center among the newly designated centers belongs, and the clustering is repeated until the center does not change any more. Characterized in that the urban air mobility vertical take-off site selection system.
According to claim 6,
Wherein the clustering result evaluation unit evaluates a clustering result of the population data corresponding to the rounded value using a silhouette technique.
According to claim 7,
The clustering result adjustment unit compares the position of the vertical take-off site according to the clustering result of the population data corresponding to the rounded value obtained by the data clustering unit and a satellite image of the expected urban air mobility use area, and compares the geographical position of the vertical take-off and landing site An urban air mobility vertical take-off landing site selection system, characterized in that it determines whether to adjust the location by reviewing the conditions.
According to claim 8,
The clustering result adjustment unit obtains geographic information including facilities, development restricted areas, no-fly areas, military restricted areas, or residential areas by analyzing the satellite image of the urban air mobility expected area for the geographical condition of the vertical takeoff and landing site, and A system for determining whether to adjust the location of the vertical take-off and landing site by comparing the latitude/longitude values of the geographic information with the latitude/longitude values of the vertical take-off and landing site.
According to claim 9,
Wherein the clustering result adjustment unit adjusts the priority or location of the vertical take-off and landing site according to the operational purpose of the urban air mobility.

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